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Bayesian optimisation for efficient material discovery: a mini review
Bayesian optimisation (BO) has been increasingly utilised to guide material discovery. While
BO is advantageous due to its sample efficiency, flexibility and versatility, it is constrained by …
BO is advantageous due to its sample efficiency, flexibility and versatility, it is constrained by …
A tutorial on derivative-free policy learning methods for interpretable controller representations
This paper provides a tutorial overview of recent advances in learning control policy
representations for complex systems. We focus on control policies that are determined by …
representations for complex systems. We focus on control policies that are determined by …
Bayesian-optimized hybrid kernel SVM for rolling bearing fault diagnosis
We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel
support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete …
support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete …
[HTML][HTML] Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-
evaluate black-box functions, with a broad range of real-world applications in science …
evaluate black-box functions, with a broad range of real-world applications in science …
Multi-fidelity active learning with gflownets
In the last decades, the capacity to generate large amounts of data in science and
engineering applications has been growing steadily. Meanwhile, machine learning has …
engineering applications has been growing steadily. Meanwhile, machine learning has …
Learning and optimization under epistemic uncertainty with Bayesian hybrid models
Abstract Hybrid (ie, grey-box) models are a powerful and flexible paradigm for predictive
science and engineering. Grey-box models use data-driven constructs to incorporate …
science and engineering. Grey-box models use data-driven constructs to incorporate …
Machine learning-assisted discovery of flow reactor designs
Additive manufacturing has enabled the fabrication of advanced reactor geometries,
permitting larger, more complex design spaces. Identifying promising configurations within …
permitting larger, more complex design spaces. Identifying promising configurations within …
Multi-fidelity data-driven design and analysis of reactor and tube simulations
Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a
framework to solve this nonlinear, computationally expensive, and derivative-free problem …
framework to solve this nonlinear, computationally expensive, and derivative-free problem …
Outlook: How I learned to Love machine learning (a personal perspective on machine learning in process systems engineering)
I have been thinking a lot about how machine learning (ML) and related areas (eg, artificial
intelligence, digitalization, and data science) are transforming and will transform our …
intelligence, digitalization, and data science) are transforming and will transform our …
[HTML][HTML] Active machine learning for chemical engineers: a bright future lies ahead!
By combining machine learning with the design of experiments, thereby achieving so-called
active machine learning, more efficient and cheaper research can be conducted. Machine …
active machine learning, more efficient and cheaper research can be conducted. Machine …